Machine Learning Engineer

As a Machine Learning Engineer, you will design, train, evaluate, and deploy deep learning models for real-world computer vision applications across mapping intelligence and robotics systems.

This is a hands-on engineering role. You will write production code, build scalable training pipelines, and improve ML infrastructure. You’ll collaborate closely with other ML engineers, software engineers, and product stakeholders to continuously improve model performance in real-world environments.

Our systems operate in challenging environments where robustness, generalization, and performance matters. The models you build will directly impact field operations and autonomous decision-making.

In this role you will:

• Design and train modern computer vision models (CNNs, Vision Transformers, foundation models) to solve novel perception tasks
  
  
• Build and maintain scalable training pipelines using PyTorch and HPC infrastructure (e.g., Slurm, distributed training)
  
  
• Develop data curation and active learning workflows
  
  
• Optimize models for deployment (ONNX, TensorRT, containerization)
  
  
• Test and validate models in both cloud and edge environments
  
  
• Build reproducible experimentation workflows (version control, experiment tracking, configuration management)
  
  
• Drive experimental cycles: define hypotheses, implement techniques from literature, evaluate results, and present recommendations
  
  
• Translate research ideas into production-ready implementations
  
  
• Design, implement, and test ML-related components and supporting software
  
  
• Perform statistical analysis and fine-tune model performance based on production and field feedback

What we’re looking for:

Required 

• 3+ years of full-time professional experience in computer vision-focused machine learning (not student internship)
  
  
• Strong PyTorch experience, including custom layers, loss functions, datasets, dataloaders, and training loops
  
  
• Experience with modern vision architectures (CNNs, ViTs, DETR-style models, foundation models)
  
  
• Experience building or contributing to production ML systems
  
  
• Solid software engineering fundamentals (testing, version control, clean code principles)
  
  
• Strong communication skills and ability to explain complex technical topics clearly
  
  
• Engineering degree or equivalent practical experience

Preferred

• Experience deploying models to edge devices (Jetson, embedded GPUs, mobile platforms)
  
  
• Experience with AWS or similar cloud infrastructure
  
  
• Experience with Docker and containerized ML workflows
  
  
• Familiarity with robotics or perception systems
  
  
• Experience owning or contributing to a production model that delivered business value

Compensation Range = $150-220K, commensurate with experience